



'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase_  :							Tuple             =    logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : str						  =					field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default='NER'     ,					metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
						_lowerCAmelCase  : bool						  =					field(default=snake_case_     ,					metadata={'help': 'Set this flag to use fast tokenization.'}      )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,					)


@dataclass
class 						lowerCamelCase_				:
						_lowerCAmelCase  : str						  =					field(
						    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}      )
						_lowerCAmelCase  : Optional[str]						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}     ,					)
						_lowerCAmelCase  : int						  =					field(
						    default=1_2_8     ,					metadata={
						        'help': (
						            'The maximum total input sequence length after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    }     ,					)
						_lowerCAmelCase  : bool						  =					field(
						    default=snake_case_     ,					metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


def        UpperCAmelCase (    ):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
							if len(sys.argv					) == 2 and sys.argv[1].endswith('''.json'''					):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
							else:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir					)
							    and os.listdir(training_args.output_dir					)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ''' --overwrite_output_dir to overcome.'''					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      import_module('''tasks'''					)
							try:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		model_args.task_type					)
														SCREAMING_SNAKE_CASE       :   TokenClassificationTask         =      token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
														    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""					)

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,		datefmt='''%m/%d/%Y %H:%M:%S'''       ,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,		)
							logger.warning(
							    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,		training_args.local_rank       ,		training_args.device       ,		training_args.n_gpu       ,		bool(training_args.local_rank != -1					)       ,		training_args.fpaa       ,		)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank					):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('''Training/evaluation parameters %s'''       ,		A					)

							# Set seed
							set_seed(training_args.seed					)

							# Prepare CONLL-2003 task
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      token_classification_task.get_labels(data_args.labels					)
							SCREAMING_SNAKE_CASE       :   Dict[int, str]         =      dict(enumerate(A					)					)
							SCREAMING_SNAKE_CASE       :   int         =      len(A					)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,		num_labels=A       ,		idalabel=A       ,		labelaid={label: i for i, label in enumerate(A					)}       ,		cache_dir=model_args.cache_dir       ,		)
							SCREAMING_SNAKE_CASE       :   Tuple         =      AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,		cache_dir=model_args.cache_dir       ,		use_fast=model_args.use_fast       ,		)
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path       ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path					)       ,		config=A       ,		cache_dir=model_args.cache_dir       ,		)

							# Get datasets
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      (
							    TokenClassificationDataset(
							        token_classification_task=A       ,		data_dir=data_args.data_dir       ,		tokenizer=A       ,		labels=A       ,		model_type=config.model_type       ,		max_seq_length=data_args.max_seq_length       ,		overwrite_cache=data_args.overwrite_cache       ,		mode=Split.train       ,		)
							    if training_args.do_train
							    else None
							)
							SCREAMING_SNAKE_CASE       :   int         =      (
							    TokenClassificationDataset(
							        token_classification_task=A       ,		data_dir=data_args.data_dir       ,		tokenizer=A       ,		labels=A       ,		model_type=config.model_type       ,		max_seq_length=data_args.max_seq_length       ,		overwrite_cache=data_args.overwrite_cache       ,		mode=Split.dev       ,		)
							    if training_args.do_eval
							    else None
							)

							def align_predictions(A	:					np.ndarray       ,		A	:					np.ndarray					) -> Tuple[List[int], List[int]]:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.argmax(A       ,		axis=2					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      preds.shape

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      [[] for _ in range(A					)]
														SCREAMING_SNAKE_CASE       :   Any         =      [[] for _ in range(A					)]

														for i in range(A					):
																					for j in range(A					):
																												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																																			out_label_list[i].append(label_map[label_ids[i][j]]					)
																																			preds_list[i].append(label_map[preds[i][j]]					)

														return preds_list, out_label_list

							def compute_metrics(A	:					EvalPrediction					) -> Dict:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      align_predictions(p.predictions       ,		p.label_ids					)
														return {
														    "accuracy_score": accuracy_score(A       ,		A					),
														    "precision": precision_score(A       ,		A					),
														    "recall": recall_score(A       ,		A					),
														    "f1": fa_score(A       ,		A					),
														}

							# Data collator
							SCREAMING_SNAKE_CASE       :   List[str]         =      DataCollatorWithPadding(A       ,		pad_to_multiple_of=8					) if training_args.fpaa else None

							# Initialize our Trainer
							SCREAMING_SNAKE_CASE       :   str         =      Trainer(
							    model=A       ,		args=A       ,		train_dataset=A       ,		eval_dataset=A       ,		compute_metrics=A       ,		data_collator=A       ,		)

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_process_zero():
																					tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
							SCREAMING_SNAKE_CASE       :   Tuple         =      {}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***'''					)

														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      trainer.evaluate()

														SCREAMING_SNAKE_CASE       :   List[str]         =      os.path.join(training_args.output_dir       ,		'''eval_results.txt'''					)
														if trainer.is_world_process_zero():
																					with open(A       ,		'''w'''					) as writer:
																												logger.info('''***** Eval results *****'''					)
																												for key, value in result.items():
																																			logger.info('''  %s = %s'''       ,		A       ,		A					)
																																			writer.write('''%s = %s\n''' % (key, value)					)

																					results.update(A					)

    # Predict
							if training_args.do_predict:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      TokenClassificationDataset(
														    token_classification_task=A       ,		data_dir=data_args.data_dir       ,		tokenizer=A       ,		labels=A       ,		model_type=config.model_type       ,		max_seq_length=data_args.max_seq_length       ,		overwrite_cache=data_args.overwrite_cache       ,		mode=Split.test       ,		)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      trainer.predict(A					)
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Tuple         =      align_predictions(A       ,		A					)

														SCREAMING_SNAKE_CASE       :   List[Any]         =      os.path.join(training_args.output_dir       ,		'''test_results.txt'''					)
														if trainer.is_world_process_zero():
																					with open(A       ,		'''w'''					) as writer:
																												for key, value in metrics.items():
																																			logger.info('''  %s = %s'''       ,		A       ,		A					)
																																			writer.write('''%s = %s\n''' % (key, value)					)

        # Save predictions
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      os.path.join(training_args.output_dir       ,		'''test_predictions.txt'''					)
														if trainer.is_world_process_zero():
																					with open(A       ,		'''w'''					) as writer:
																												with open(os.path.join(data_args.data_dir       ,		'''test.txt'''					)       ,		'''r'''					) as f:
																																			token_classification_task.write_predictions_to_file(A       ,		A       ,		A					)

							return results
def        UpperCAmelCase (    A	:					List[Any]					):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()









'''simple docstring'''



from math import pow
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		A	:					int       ,		):
							if current_sum == needed_sum:
														# If the sum of the powers is equal to needed_sum, then we have a solution.
														solutions_count += 1
														return current_sum, solutions_count

							SCREAMING_SNAKE_CASE       :   Tuple         =      int(pow(A       ,		A					)					)
							if current_sum + i_to_n <= needed_sum:
														# If the sum of the powers is less than needed_sum, then continue adding powers.
														current_sum += i_to_n
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
														current_sum -= i_to_n
							if i_to_n < needed_sum:
														# If the power of i is less than needed_sum, then try with the next power.
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      backtrack(
														    A       ,		A       ,		current_number + 1       ,		A       ,		A					)
							return current_sum, solutions_count
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							if not (1 <= needed_sum <= 1000 and 2 <= power <= 10):
														raise ValueError(
														    '''Invalid input\n'''
														    '''needed_sum must be between 1 and 1000, power between 2 and 10.'''					)

							return backtrack(A       ,		A       ,		1       ,		0       ,		0					)[1]  # Return the solutions_count


if __name__ == "__main__":
							import doctest

							doctest.testmod()




